TY - GEN
T1 - LIDAR-GUIDED VEGETATION VERTICAL STRUCTURE CLASSIFICATION USING POLINSAR DATA
AU - Zhang, Shurong
AU - Zhang, Lamei
AU - Zou, Bin
AU - Fu, Haiqiang
AU - Zhu, Jianjun
N1 - Publisher Copyright:
©2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Understanding the vertical structure of vegetation is crucial for applications such as tree height inversion, biomass estimation, and terrain detection in forests. A novel approach for the classification of vegetation vertical structure is presented, utilizing multi-source data integration. The analysis begins with lidar waveform characteristics, defining vegetation layers based on peak count. Employing machine learning, a correlation is established between polarimetric features, polarimetric interferometric features, and vertical vegetation structure. The study explores the potential of spatial information extraction through combined polarimetric and polarimetric interferometric SAR techniques. This innovative method offers a fresh perspective on vertical vegetation classification.
AB - Understanding the vertical structure of vegetation is crucial for applications such as tree height inversion, biomass estimation, and terrain detection in forests. A novel approach for the classification of vegetation vertical structure is presented, utilizing multi-source data integration. The analysis begins with lidar waveform characteristics, defining vegetation layers based on peak count. Employing machine learning, a correlation is established between polarimetric features, polarimetric interferometric features, and vertical vegetation structure. The study explores the potential of spatial information extraction through combined polarimetric and polarimetric interferometric SAR techniques. This innovative method offers a fresh perspective on vertical vegetation classification.
KW - classification
KW - Lidar
KW - PolInSAR
KW - vertical structure of vegetation
UR - https://www.scopus.com/pages/publications/85208468330
U2 - 10.1109/IGARSS53475.2024.10642622
DO - 10.1109/IGARSS53475.2024.10642622
M3 - 会议稿件
AN - SCOPUS:85208468330
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 5369
EP - 5372
BT - IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2024 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2024
Y2 - 7 July 2024 through 12 July 2024
ER -